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Thompson, J., Bengio, Y. and Schoenwiesner, M. (2019) The Effect of Task and Training on Intermediate Representations in Convolutional Neural Networks Revealed with Modified RV Similarity Analysis. 2019 Conference on Cognitive Computational Neuroscience, Berlin, 13-16 September 2019.
https://doi.org/10.32470/CCN.2019.1300-0

has been cited by the following article:

• TITLE: Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

  AUTHORS: Daniel Schilling Weiss Nguyen, Richard Odigie

  KEYWORDS: Artificial Intelligence, Deep Learning, General-Purpose Learning Agent, Generalizability, Algorithmic Flexibility, Internal Autonomy

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.9, September 28, 2023

  ABSTRACT: Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.